Acknowledgments. Variation in Drug Response 7/15/2015. Personalized / Precision Medicine: Genomes Guiding Treatment

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1 Personalized / Precision Medicine: Genomes Guiding Treatment Sharon E. Plon, MD, PhD Professor Baylor College of Medicine splon@bcm.edu Acknowledgments Many of the slides shown today were created by Teri Klein and colleagues! They appreciate feedback on the use of these NIH supported resources. Pharm GK B Pharmacogenomics Knowledge Base Teri Klein (Stanford) director of Pharmgkb which is financially supported by NIH/NIGMS (R24 GM61374) - Contact: feedback@pharmgkb.org Variation in Drug Response Patients with diagnosis X all get the same treatment Some respond well Some have poor or no response Some experience adverse reactions 1

2 Factors Affecting Drug Response concomitant drugs herbal supplements Environment diet disease weight gender age Clinical Factors Genetics Definitions Pharmacogenetics: study of individual genedrug interactions, usually one or two genes that have dominant effect on a drug response (SIMPLE relationship) Pharmacogenomics: study of genomic influence on drug response, often using highthroughput data: sequencing, SNP chip, expression, proteomic (COMPLEX interactions) Clinical Promises of PGx Personalized Medicine: Selecting the right dose of the right drug to the right patient at the right time Today: - empirical selection of drug Try 1st Drug A Assess effect Drug B Efficacy may vary widely Adverse effects are common & unpredictable Future: - Genetically guided drug and dose selection Drug A Test Drug A ½ dose Drug B Avoid adverse drug reactions Maximize drug efficacy Select responsive patients 2

3 White House Precision Medicine Initiative Different faces of genomic variability Drug Metabolism Variants that impact transformation of a pro-drug into active drug moieties Variants that accelerate drug degradation Drug Targets Late Effects Somatic mutations in tumors that make the tumor susceptible to specific treatments Germline mutations that mediate resistance or sensitivity to agents, e.g. PARP inhibitors/brca tumors Variants in genes expressed in target tissue that make late effects more or less likely Variants that impact the degree of damage caused by therapeutic agents across normal tissues Why do we refer to them as variants? The actual genetic changes studied can vary from common single nucleotide polymorphisms (SNP) to specific translocations found in tumor tissues, e.g. Philadelphia translocation, that make tumors sensitive to imatinib Variant refers to any sequence change different than the reference genome Maybe common or rare; benign or pathogenic 3

4 rs sample SNP Minor Allele Frequency in Control Populations!la93= silent A = A or reference allele; G = B allele Star alleles are different language for SNPS Downloadable index tables map SNPs/variants to * allele nomenclature Importance of studying diverse populations and knowing what population is being studied A key issue when studying genetic variation is to take into account the impact of the population on data analysis. This is often done by matching cases/controls based on self-described race or ethnicity. It is important to realize that some genetic changes will have a different impact on drug metabolism in different populations: Unfortunately, much of the published literature focuses on populations that are primarily European Caucasian. Increasing emphasis on expanding research to more diverse patient cohorts. 4

5 Replication, replication, replication A major problem in many older genomic studies was the lack of statistical power. Unfortunately, these studies were often highlighted in the press and then found to not be replicated in larger studies causing confusion! There is an increasing emphasis, particularly in genomescale studies, to replicate the positive findings from the first data analysis in a 2 nd dataset before publication. Replica sets can be similar case/control cohorts obtained from collaborators Don t make clinical decisions based on a single finding. NIH is putting increasing emphasis on training all scientists in publishing more reproducible data! Examples of three different types of pharmacogenomic studies based on childhood cancer treatment trials Germline mutation confers toxicity by altering drug metabolism Drug Metabolism TPMT variants that impact enzymes which normally convert an active drug into inactive compound Evaluated patients at St. Jude Children s Research Hospital with excess toxicity to 6-MP Found ~10% of population is heterozygous for this variant. 1% are homozygous and completely inactivate the TMPT enzyme Thus, not all important variants are rare Led to FDA labeling regarding azathioprine and other analogs 5

6 Germline mutations in TPMT make patients more sensitive to 6-MP and 6-TG treatments FDA Drug Labeling for Azathioprine Germline mutation alters target tissue response to drug to confer toxicity Late Effects Variants in genes expressed in target tissue that make late effects more or less likely Anthracyclinerelated cardiomyopathy Case-control study design in the Children s Oncology Group; cases patients with anthracycline-related cardiomyopathy, controls patients w/o cardiomyopathy Did a genome-wide look for variants that impact toxicity. Followed by replicate set 6

7 Results for test for a trend in the gene-environment (anthracycline) interaction between cardiomyopathy and each single nucleotide polymorphism (SNP) measured in the genomewide association study. Xuexia Wang et al. JCO 2014;32: by American Society of Clinical Oncology Risk of cardiomyopathy by anthracycline dose and genotype status (AA, GA, GG). Xuexia Wang et al. JCO 2014;32: by American Society of Clinical Oncology Somatic mutation confers resistance Drug Metabolism Somatic mutations in the leukemia cells themselves impact transformation of pro-drug into active drug moieties Two groups analyzed leukemia cells in the subgroup of patients that relapsed after standard chemotherapy. They found a consistent set of mutations that activated one enzyme (NT5C2) which increased active drugs becoming inactive metabolites 7

8 Activating somatic mutations in NT5C2 confer resistance ALL relapse Adenosine metabolism pathway Adenosine or AdoMet MT AdoHyc SAHH nucleoside analogs ADA Inosine AK NT5C1A Hypoxanthine AMPDA AMP IMP XO RNA synthesis Xanthine DNA synthesis ADP XO DNA Repair ATP Uric acid Cellular metabolism camp Pharmacogenomics/Precision Medicine There is an explosion of information about how genetic changes impact all aspects of the response to medication. There is increasing pressure to translate these findings rapidly into clinical care. However, it takes very large studies over time to reach the point of clinical implementation of this work and insurance coverage. 8

9 PHARMACOGENOMIC RESOURCES AVAILABLE TO PHYSICIANS NIH Supported PharmGKB Database Mission Collect, encode and disseminate knowledge about the impact of human genetic variation on drug response Research Manual curation of PGx literature Drug-centered pathways Important PGx gene summaries Implementation Clinical summaries of PGx variants Drug label annotations CPIC guidelines PGx data consortia (IWPC, ITPC, etc.) Genome annotations PharmGKB Knowledge Pyramid 9

10 The following icons indicate that data of a certain type is available: Variant Annotation Clinical Annotation Pathway VIP Summary Annotated Drug Label Dosing Guideline 50 VIP genes Clinical annotation is a summary of the clinical impact of a genomic variant on drug response phenotype. Strength based on: Implementation Statistics Replications Population size 10

11 PharmGKB in 15 seconds or less WHY? 1. You want to advise a patient regarding potential therapies where your patient (or you) knows their CYP2D6 status. 2. You want to quickly look up information about a gene or SNP during a seminar on their mobile device (e.g., rs ). Desktop View iphone View Summary: PharmGKB and resources for PGx PGx - variation in genetics impacts drug response Drug efficacy and safety can be improved via genome-informed drug use PharmGKB curates knowledge about the impact of human genetic variation on drug response and its clinical implementation Long term goal: genome-informed drug use that increases efficacy and decreases side effects 11

12 CLINICAL PHARMACOGENOMIC IMPLEMENTATION (MOVING FROM PHARMGKB TO THE CLINIC) Clinical Pharmacogenomics Implementation Consortium (CPIC) Established by the Pharmacogenomics Research Network and PharmGKB GOAL: Address some of the barriers to implementation of PGx tests in clinical practice HOW available genetic test results should be used to optimize drug therapy, not WHETHER tests should be ordered Guidelines peer-reviewed and published in Clinical Pharmacology and Therapeutics simultaneous posting on PharmGKB Heart of the CPIC Guideline There must be a diplotype that is so high risk for the gene that if you knew the patient had that diplotype, you would take it into account in choosing which drug or choosing a dose If there are no recommendations that you are willing to make based on at least one high-risk diplotype, then it is probably not worth writing the CPIC guideline A knowledgeable clinician needs to buy into at least ONE recommendation 12

13 Grading Supporting Evidence High: Evidence includes consistent results from welldesigned, well-conducted studies. Moderate: Evidence is sufficient to determine effects, but the strength of the evidence is limited by the number, quality, or consistency of the individual studies; generalizability to routine practice; or indirect nature of the evidence. Weak: Evidence is insufficient to assess the effects on health outcomes because of limited number or power of studies, important flaws in their design or conduct, gaps in the chain of evidence, or lack of information. Clinical Pharmacogenomics Implementation Consortium (CPIC) Thiopurines/TPMT Warfarin/CYP2C9 & VKORC1 Codeine/CYP2D6 Clopidogrel/CYP2C19 Carbamazepine/HLA-B*1502 Abacavir/HLA-B*5701 Allopurinol/HLA-B*1501 Simvastatin/SLCO1B1 Tricyclic Acids/CYP2D6/CYP2C19 Capecitabine & Fluorouracil/DPYD PegIntron/ILFN3 (IL28B) others in the pipeline CPIC Guidelines for Azathioprine 13

14 DOES PGX TRULY HAVE A ROLE IN PERSONALIZED MEDICINE? Case 1: Warfarin Genetics Three genes known to affect warfarin dose: CYP2C9, VKORC1 and CYP4F2 Know variation in CYP2C9 affects PK But only accounted for < 15% of variation Vitamin K epoxide reductase (VKORC1) affects PD Found in rat-poison-resistant rats Non-coding SNP explains 35% of variation VKORC1: G>A allele 14

15 Warfarin Response Clinical and environmental factors Age, gender, ethnicity, BMI Co-administered drugs Co-morbidity Food (Vitamin K intake) Smoking Genetic factors CYP2C9 and VKORC1 Other factors (up to 40%) CYP2C9 (up to 15%) Age, sex, weight (10-20%) VKORC1 (up to 25%) Warfarin Label Changes for PGx In 2007 and 2010, FDA updated label a patient s CYP2C9 and VKORC1 genotype information, when available, can assist in selection of the starting dose. provides initial dosage recommendations for different variant combinations Warfarin Dosing Calculator 15

16 Warfarin Consortium and Recent Results Complicated Picture IWPC retrospective study better dose prediction with PGx COAG trial no increase of time in therapeutic range with PGx dosing EU-PACT Warfarin trial PGx group had higher mean percentage of time in therapeutic range EU-PACT Acenocoumarol and Phenprocoumon trial no increase of time in therapeutic range with PGx dosing GIFT trial will focus on clinical outcomes with PGx CANCER GENOMICS USING TARGETED THERAPY Summary of Cancer Mutations Adult Cancers Large number of somatic mutations per tumor Pediatric Cancers Very small number of somatic mutations per tumor Much larger numbers of patients with Small numbers of patients that make each tumor type to facilitate clinical development of targeted drugs less trials favorable Apparently greater number of activating mutations in kinases, e.g. EGFR and BRAF that are targets of FDA approved drugs Potentially greater proportion of events such as translocations as drivers in cancer Many mutations appear in nontargeted genes, e.g. histone H3F3A 16

17 Mutated oncogenes have diverse functions which can be targeted ERBB2/Her2 RET by specific cancer treatment 1. Growth factors 2. Growth factor receptors 3. Signaling molecules 4. Transcription factors Reflects the many different ways to convert a normal well regulated cell into a tumor cell. Protein Receptors on Cell Surface Growth signals from the body are transmitted through the cytoplasm to the nucleus Ras, BRAF and ALK Cell Division occurs in Nucleus Myc & transcription factors BCR-Abl Philadelphia Chromosome from Goldman & Melo, NEJM (2003) Imatinib targets fusion kinase in CML/rare pediatric ALL from Goldman & Melo, NEJM (2003) 17

18 Chr 2 Inversion - EML4-ALK Fusion Chromosome 2p KD ALK EML4 Normal cell Inversion KD ALK EML4 Tumor cell Inversion event is seen in ~3% of non-small cell lung cancer. Results in activation of ALK because the EML4 domain aids in heterodimerization. Response to ALK Inhibition. Kwak EL et al. N Engl J Med 2010;363: Structural organization of ALK and its fusion proteins. Mano H Cancer Discovery 2012;2: by American Association for Cancer Research 18

19 Therapeutic Implications Early trials supported the use of ALK inhibitors for the treatment of EML4-ALK positive NSCLC. August 26, 2011, the FDA approved crizotinib as a treatment for EML4- ALK rearranged NSCLCs. Targeting of activated ALK may be successful therapeutic approach to diverse tumors types that contain ALK alterations. Pediatric Phase 1 trial of crizotinib demonstrated activity against tumors with rearranged ALK. Less clear efficacy for tumors with missense mutations (Yosse et al., Lancet Oncology, 2013). Management of BRCA1/BRCA2 Mutation Carriers Early surveillance starting age 25 with breast exams and MRI imaging. Prophylactic salpingo-opherectomy recommended after childbearing (ages 35-40) No proven ovarian cancer screening methods CA125 serum marker and transvaginal ultrasound recommended only if oopherectomy refused. Chemoprevention: Tamoxifen and raloxifene reduce breast cancer risk in the general population but it is less clear their efficacy in mutation carriers. Now targeted treatment of ovarian cancer in patients with BRCA1/2 mutations Synthetic Lethal Interaction PARP inhibition: targeting the Achilles' heel of DNA repair to treat germline and sporadic ovarian cancers. Carden, Craig; Yap, Timothy; Kaye, Stan, Current Opinion in Oncology. 22(5): , September

20 Olaparib approved by FDA for ovarian cancer in 2015 This is an example where the cancer treatment is based on patient with an inherited BRCA1/2 mutation or more rarely one that occurs in the tumor itself. Likely to lead to increased use of genetic testing for ovarian cancer patients: Well documented that ~10% of ovarian cancer patients carry a germline BRCA1/2 mutation Sampling FDA approved cancer drugs targeting specific genetic changes Drug Target Tumors - off label use in italics Imatinib BCR-ABL and C-KIT CML,GIST, PH+-ALL Trastuzumab ERBB2/HER2/neu Breast cancer, gastric or GEJ junction cancer Crizotinib EML4-ALK ALK missense mutation NSCLC Neuroblastoma and other ALK+ tumors Vermurafinib BRAF V600E Melanoma Erlotinib EGFR missense Lung cancer Vandetanib RET missense Medullary thyroid cancer Germline status Everolimus TSC1/TSC mutant Subependymal giant cell astrocytoma Olaparib BRCA1/2 germline mutation Ovarian cancer (PARP inhibitor) Questions 20